Experimental setting.
All object detectors are trained in their default settings on Tesla V100 GPUs. The SSDs follows the basic training recipe in mmdetection [1]}. The lightweight Faster RCNNs are trained with a 1\(\times \)  training schedule for 12 epochs.
The details for the training settings of each model are provided in the supplementary material. We use a ResNet50 with input resolution \(112 \times 112\)  as classification teacher for all student detectors. We report the mean average precision (mAP) and mean average recall (mAR) for intersection over unions (IoUs) in [0.5:0.95], the APs at IoU=0.5 and 0.75, and the APs and ARs for small, medium and large objects.
